Spatial Interaction Models (SIMs) are mathematical models for estimating movement between spatial entities developed by Alan Wilson in the late 1960s and early 1970, with considerable uptake and refinement for transport modelling since then Boyce and Williams (2015). There are four main types of traditional SIMs (Wilson 1971):

Unconstrained

Production-constrained

Attraction-constrained

Doubly-constrained

An early and highly influential type of SIM was the ‘gravity model’, defined by Wilson (1971) as follows (in a paper that explored many iterations on this formulation):

\[ T_{i j}=K \frac{W_{i}^{(1)} W_{j}^{(2)}}{c_{i j}^{n}} \]

“where \(T_{i j}\) is a measure of the interaction between zones \(i\) and \(W_{i}^{(1)}\) is a measure of the ‘mass term’ associated with zone \(z_i\), \(W_{j}^{(2)}\) is a measure of the ‘mass term’ associated with zone \(z_j\), and \(c_{ij}\) is a measure of the distance, or generalised cost of travel, between zone \(i\) and zone \(j\)”. \(K\) is a ‘constant of proportionality’ and \(n\) is a parameter to be estimated.

Redefining the \(W\) terms as \(m\) and \(n\) for origins and destinations respectively (Simini et al. 2012), this classic definition of the ‘gravity model’ can be written as follows:

\[ T_{i j}=K \frac{m_{i} n_{j}}{c_{i j}^{n}} \]

For the purposes of this project, we will focus on production-constrained SIMs. These can be defined as follows (Wilson 1971):

\[ T_{ij} = A_iO_in_jf(c_{ij}) \]

where \(A\) is a balancing factor defined as:

\[ A_{i}=\frac{1}{\sum_{j} m_{j} \mathrm{f}\left(c_{i j}\right)} \]

\(O_i\) is analogous to the travel demand in zone \(i\), which can be roughly approximated by its population.

More recent innovations in SIMs including the ‘radiation model’ Simini et al. (2012). See Lenormand, Bassolas, and Ramasco (2016) for a comparison of alternative approaches.

Before using the functions in this or other packages, it may be worth
implementing SIMs from first principles, to gain an understanding of how
they work. The code presented below was written before the functions in
the `simodels`

package were developed, building on (**dennett_estimating_2012?**). The
aim is to demonstrate a common way of running SIMs, in a for loop,
rather than using vectorised operations (used in the
`simodels`

package) which can be faster.

```
library(tmap)
library(dplyr)
library(ggplot2)
```

```
= simodels::si_zones
zones = simodels::si_centroids
centroids = simodels::si_od_census
od tm_shape(zones) + tm_polygons("all", palette = "viridis")
```

```
= od::points_to_od(centroids)
od_df = od::odc_to_sfc(od_df[3:6])
od_sfc ::st_crs(od_sfc) = 4326
sf$length = sf::st_length(od_sfc)
od_df= od_df %>% transmute(
od_df length = as.numeric(length) / 1000,
O, D, flow = NA, fc = NA
)= sf::st_sf(od_df, geometry = od_sfc, crs = 4326) od_df
```

An unconstrained spatial interaction model can be written as follows,
with a more-or-less arbitrary value for `beta`

which can be
optimised later:

```
= 0.3
beta = 1
i = 2
j for(i in seq(nrow(zones))) {
for(j in seq(nrow(zones))) {
= zones$all[i]
O = zones$all[j]
n = which(od_df$O == zones$geo_code[i] & od_df$D == zones$geo_code[j])
ij $fc[ij] = exp(-beta * od_df$length[ij])
od_df$flow[ij] = O * n * od_df$fc[ij]
od_df
}
}= od_df %>%
od_top filter(O != D) %>%
top_n(n = 2000, wt = flow)
tm_shape(zones) +
tm_borders() +
tm_shape(od_top) +
tm_lines("flow")
```